Abstract: The detection of evolving cyber threats proves challenging for traditional anomaly detection because signature-based models do not identify new or zero-day attacks. This research develops an AI Transformer-based system with Bidirectional Encoder Representations from Transformers (BERT) technology with Zero-Shot Learning (ZSL) for real-time network system anomaly detection while solving these security challenges. The goal positions the development of an effective alerting system that detects Incident response and proactive defenses cyber threats both known and unknown while needing minimal human input. The methodology uses BERT to transform textual attack descriptions found in CVEs alongside MITRE ATT&CK TTPs into multidimensional embedding features. Visual embeddings generated from textual documents undergo comparison analysis with current network traffic data containing packet flow statistics and connection logs through the cosine similarity method to reveal potential suspicious patterns. The Zero-Shot Learning extension improves the system by enabling threat recognition of new incidents when training data remains unlabeled through its analysis of semantic links between familiar and unfamiliar attack types. Here utilizes three different tools that include Python for programming purposes alongside BERT for embedding analytics and cosine similarity for measuring embedded content similarities. Numerical experiment outcomes validate the proposed framework by achieving a 99.7% accuracy measure with 99.4% precision, 98.8% recall while maintaining a sparse 1.1% false positive rate. The system operates with a detection latency of just 45ms, making it suitable for dynamic cybersecurity environments. The results indicate that the AI-driven Transformer framework outperforms conventional methods, providing a robust, real-time solution for anomaly detection that can adapt to evolving cyber threats without extensive manual intervention.

Santosh Reddy P, Tarunika Chaudhari, Sanjiv Rao Godla, Janjhyam Venkata Naga Ramesh, Elangovan Muniyandy, A. Smitha Kranthi and Yousef A.Baker El-Ebiary, “AI-Driven Transformer Frameworks for Real-Time Anomaly Detection in Network Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.01602111

@article{P2025,
title = {AI-Driven Transformer Frameworks for Real-Time Anomaly Detection in Network Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01602111},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01602111},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Santosh Reddy P and Tarunika Chaudhari and Sanjiv Rao Godla and Janjhyam Venkata Naga Ramesh and Elangovan Muniyandy and A. Smitha Kranthi and Yousef A.Baker El-Ebiary}
}